Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Kuo, Chung‐Feng Jeffrey; Wu, Han-Cheng

doi:10.1002/ima.22307

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…There is an area in 2617 images, three areas in 118 cases, and four areas in 12 cases. Pneumonia is usually distributed in the left and right lobes, and its grayscale is blurred, which is difficult to identify directly [ 23 ]. The histogram of the average gray value distribution of the target area is shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN

Yao

Chen

Tian

et al. 2021

Computational and Mathematical Methods in Medicine

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Pneumonia remains a threat to human health; the coronavirus disease 2019 (COVID-19) that began at the end of 2019 had a major impact on the world. It is still raging in many countries and has caused great losses to people’s lives and property. In this paper, we present a method based on DeepConv-DilatedNet of identifying and localizing pneumonia in chest X-ray (CXR) images. Two-stage detector Faster R-CNN is adopted as the structure of a network. Feature Pyramid Network (FPN) is integrated into the residual neural network of a dilated bottleneck so that the deep features are expanded to preserve the deep feature and position information of the object. In the case of DeepConv-DilatedNet, the deconvolution network is used to restore high-level feature maps into its original size, and the target information is further retained. On the other hand, DeepConv-DilatedNet uses a popular fully convolution architecture with computation shared on the entire image. Then, Soft-NMS is used to screen boxes and ensure sample quality. Also, K-Means++ is used to generate anchor boxes to improve the localization accuracy. The algorithm obtained 39.23% Mean Average Precision (mAP) on the X-ray image dataset from the Radiological Society of North America (RSNA) and got 38.02% Mean Average Precision (mAP) on the ChestX-ray14 dataset, surpassing other detection algorithms. So, in this paper, an improved algorithm that can provide doctors with location information of pneumonia lesions is proposed.

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Section: Methodsmentioning

confidence: 99%

Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN

Yao

Chen

Tian

et al. 2021

Computational and Mathematical Methods in Medicine

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“…1. Equalizing the histogram [35,36] of an image is a commonly used processing technique in image enhancement, which is an effective method for adjusting image The grayscale histogram of an image can be abstracted as a one-dimensional discrete function, which can be written as Eq. ( 8).…”

Section: Image Enhancementmentioning

confidence: 99%

A new surface roughness measurement method based on QR-SVM

Yu,

Li,

Sheng

et al. 2023

Preprint

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This study proposes a method for detecting surface roughness in machining, which solves the problem of low detection accuracy caused by a small sample size based on machine vision detection. The fusion of QR and Support Vector Machine (SVM) methods is used to detect surface roughness. Firstly, a contact roughness detector is used to measure the surface roughness value, and a CCD is used to obtain the processed surface image to obtain the sample. Secondly, the QR decomposition method is improved to generate virtual samples and expand the sample size. Extract the texture feature values of the image using the gray level co-occurrence matrix, and establish the correlation between roughness and texture features. Finally, support vector machines are used to classify the surface roughness of mechanical machining. The experimental results show that the accuracy of the surface roughness detection method based on machine vision has increased from 80.6–96.5%, proving the feasibility of this method and providing a theoretical basis for on-site detection of small sample surface roughness. This method has certain engineering application potential.

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“…SPECT images are usually integer gray values, and all results must be rounded to the nearest integer value. Therefore, when the strict monotonic condition is not satisfied, the method of finding the closest integer match is used to solve the problem of non-unique inverse transformation (10). Figure 2 is the effect of histogram equalization on the improvement of SPECT image quality and the RGB color distribution diagram of the image before and after adjustment.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Classification and Diagnosis of Residual Thyroid Tissue in SPECT Images Based on Fine-Tuning Deep Convolutional Neural Network

Guo

et al. 2021

Front. Oncol.

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Patients with thyroid cancer will take a small dose of 131I after undergoing a total thyroidectomy. Single-photon emission computed tomography (SPECT) is used to diagnose whether thyroid tissue remains in the body. However, it is difficult for human eyes to observe the specificity of SPECT images in different categories, and it is difficult for doctors to accurately diagnose the residual thyroid tissue in patients based on SPECT images. At present, the research on the classification of thyroid tissue residues after thyroidectomy is still in a blank state. This paper proposes a ResNet-18 fine-tuning method based on the convolutional neural network model. First, preprocess the SPECT images to improve the image quality and remove background interference. Secondly, use the preprocessed image samples to fine-tune the pretrained ResNet-18 model to obtain better features and finally use the Softmax classifier to diagnose the residual thyroid tissue. The method has been tested on SPECT images of 446 patients collected by local hospital and compared with the widely used lightweight network SqueezeNet model and ShuffleNetV2 model. Due to the small data set, this paper conducted 10 random grouping experiments. Each experiment divided the data set into training set and test set at a ratio of 3:1. The accuracy and sensitivity rates of the model proposed in this paper are 96.69% and 94.75%, which are significantly higher than other models (p < 0.05). The specificity and precision rates are 99.6% and 99.96%, respectively, and there is no significant difference compared with other models. (p > 0.05). The area under the curve of the proposed model, SqueezeNet, and ShuffleNetv2 are 0.988 (95% CI, 0.941–1.000), 0.898 (95% CI, 0.819–0.951) (p = 0.0257), and 0.885 (95% CI, 0.803–0.941) (p = 0.0057) (p < 0.05). We prove that this thyroid tissue residue classification system can be used as a computer-aided diagnosis method to effectively improve the diagnostic accuracy of thyroid tissue residues. While more accurately diagnosing patients with residual thyroid tissue in the body, we try our best to avoid the occurrence of overtreatment, which reflects its potential clinical application value.

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Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Cited by 10 publications

References 44 publications

Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN

Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN

A new surface roughness measurement method based on QR-SVM

Classification and Diagnosis of Residual Thyroid Tissue in SPECT Images Based on Fine-Tuning Deep Convolutional Neural Network

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